from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match

print_time_report()

Time report

sklearnex_KMeans_short: 0h 0m 2s

sklearnex_Ridge: 0h 0m 2s

KMeans_short: 0h 0m 3s

sklearnex_LogisticRegression: 0h 0m 6s

sklearnex_KMeans_tall: 0h 0m 11s

Ridge: 0h 0m 12s

KMeans_tall: 0h 0m 28s

LogisticRegression: 0h 0m 28s

sklearnex_KNeighborsClassifier_kd_tree: 0h 0m 37s

KNeighborsClassifier_kd_tree: 0h 2m 54s

xgboost: 0h 5m 1s

catboost_symmetric: 0h 5m 7s

catboost_lossguide: 0h 5m 9s

lightgbm: 0h 5m 18s

HistGradientBoostingClassifier: 0h 5m 29s

sklearnex_KNeighborsClassifier: 0h 6m 28s

KNeighborsClassifier: 0h 28m 25s

total: 1h 6m 7s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.8.0-1036-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.3",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.21.0",
    "scipy": "1.7.0",
    "Cython": null,
    "pandas": "1.3.0",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.188	0.0	4.254	0.0	1	1	NaN	NaN	0.571	0.0	0.330	0.0	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.180	0.0	4.450	0.0	-1	5	NaN	NaN	0.575	0.0	0.313	0.0	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.158	0.0	5.057	0.0	-1	1	NaN	NaN	0.606	0.0	0.261	0.0	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.150	0.0	5.334	0.0	1	5	NaN	NaN	0.558	0.0	0.269	0.0	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.157	0.0	5.109	0.0	-1	100	NaN	NaN	0.574	0.0	0.273	0.0	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.192	0.0	4.176	0.0	1	100	NaN	NaN	0.575	0.0	0.333	0.0	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.064	0.0	0.249	0.0	1	1	NaN	NaN	0.123	0.0	0.523	0.0	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.069	0.0	0.232	0.0	-1	5	NaN	NaN	0.122	0.0	0.566	0.0	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.067	0.0	0.239	0.0	-1	1	NaN	NaN	0.112	0.0	0.595	0.0	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.067	0.0	0.239	0.0	1	5	NaN	NaN	0.121	0.0	0.553	0.0	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.060	0.0	0.267	0.0	-1	100	NaN	NaN	0.119	0.0	0.503	0.0	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.071	0.0	0.226	0.0	1	100	NaN	NaN	0.111	0.0	0.638	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	19.433	0.209	0.0	0.019	1	1	0.712	0.949	4.659	0.038	4.171	0.056	See	See
2	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.268	0.010	0.0	0.268	1	1	0.000	1.000	0.110	0.002	2.439	0.110	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	46.744	0.000	0.0	0.047	-1	5	0.820	0.820	4.538	0.030	10.300	0.069	See	See
5	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.237	0.021	0.0	0.237	-1	5	1.000	1.000	0.111	0.002	2.142	0.195	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	35.229	0.000	0.0	0.035	-1	1	0.712	0.719	4.616	0.073	7.632	0.121	See	See
8	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.228	0.014	0.0	0.228	-1	1	0.000	1.000	0.109	0.003	2.088	0.137	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	31.417	0.000	0.0	0.031	1	5	0.820	0.949	4.674	0.020	6.721	0.029	See	See
11	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.258	0.005	0.0	0.258	1	5	1.000	1.000	0.108	0.002	2.377	0.068	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	47.181	0.000	0.0	0.047	-1	100	0.922	0.719	4.564	0.029	10.339	0.065	See	See
14	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.229	0.028	0.0	0.229	-1	100	1.000	1.000	0.110	0.004	2.080	0.260	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	31.331	0.000	0.0	0.031	1	100	0.922	0.820	4.596	0.040	6.817	0.060	See	See
17	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.263	0.006	0.0	0.263	1	100	1.000	1.000	0.118	0.007	2.223	0.140	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	13.147	0.193	0.0	0.013	1	1	0.972	0.984	1.096	0.008	12.000	0.198	See	See
20	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.021	0.003	0.0	0.021	1	1	1.000	1.000	0.005	0.000	3.820	0.623	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	36.539	0.000	0.0	0.037	-1	5	0.984	0.983	1.022	0.009	35.755	0.313	See	See
23	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.034	0.004	0.0	0.034	-1	5	1.000	1.000	0.005	0.000	6.685	0.862	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	28.329	0.182	0.0	0.028	-1	1	0.972	0.970	1.019	0.016	27.790	0.467	See	See
26	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.025	0.003	0.0	0.025	-1	1	1.000	1.000	0.005	0.001	4.671	0.709	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	21.393	0.217	0.0	0.021	1	5	0.984	0.984	1.102	0.016	19.405	0.341	See	See
29	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.024	0.001	0.0	0.024	1	5	1.000	1.000	0.005	0.000	4.721	0.336	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	34.551	0.000	0.0	0.035	-1	100	0.984	0.970	1.017	0.015	33.959	0.490	See	See
32	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.032	0.003	0.0	0.032	-1	100	1.000	1.000	0.005	0.000	6.730	0.728	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	20.482	0.436	0.0	0.020	1	100	0.984	0.983	1.013	0.023	20.224	0.632	See	See
35	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.022	0.001	0.0	0.022	1	100	1.000	1.000	0.005	0.000	4.361	0.311	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.569	0.0	0.022	0.0	-1	1	NaN	NaN	0.898	0.0	3.976	0.0	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.262	0.0	0.025	0.0	1	1	NaN	NaN	0.844	0.0	3.863	0.0	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.127	0.0	0.026	0.0	1	5	NaN	NaN	0.908	0.0	3.444	0.0	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.274	0.0	0.024	0.0	1	100	NaN	NaN	0.843	0.0	3.885	0.0	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.168	0.0	0.025	0.0	-1	5	NaN	NaN	0.894	0.0	3.543	0.0	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.321	0.0	0.024	0.0	-1	100	NaN	NaN	0.840	0.0	3.954	0.0	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.804	0.0	0.020	0.0	-1	1	NaN	NaN	0.548	0.0	1.469	0.0	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.877	0.0	0.018	0.0	1	1	NaN	NaN	0.534	0.0	1.641	0.0	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.900	0.0	0.018	0.0	1	5	NaN	NaN	0.554	0.0	1.625	0.0	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.946	0.0	0.017	0.0	1	100	NaN	NaN	0.576	0.0	1.643	0.0	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.921	0.0	0.017	0.0	-1	5	NaN	NaN	0.590	0.0	1.562	0.0	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.930	0.0	0.017	0.0	-1	100	NaN	NaN	0.533	0.0	1.746	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.486	0.010	0.0	0.000	-1	1	0.963	0.972	0.256	0.008	1.900	0.073	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.001	0.0	0.003	-1	1	1.000	1.000	0.000	0.000	10.807	4.814	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.835	0.018	0.0	0.001	1	1	0.963	0.972	0.271	0.013	3.084	0.159	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.0	0.001	1	1	1.000	1.000	0.000	0.000	2.706	1.362	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	1.620	0.041	0.0	0.002	1	5	0.973	0.947	0.142	0.012	11.416	1.004	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.0	0.001	1	5	1.000	1.000	0.000	0.000	4.268	1.919	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	5.394	0.125	0.0	0.005	1	100	0.975	0.974	0.771	0.013	6.994	0.199	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.001	0.0	0.003	1	100	1.000	1.000	0.001	0.000	4.534	1.966	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.853	0.018	0.0	0.001	-1	5	0.973	0.974	0.781	0.029	1.093	0.047	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.001	0.0	0.003	-1	5	1.000	1.000	0.001	0.000	4.784	1.858	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.850	0.026	0.0	0.003	-1	100	0.975	0.947	0.132	0.003	21.582	0.501	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.005	0.001	0.0	0.005	-1	100	1.000	1.000	0.000	0.000	18.856	9.121	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.036	0.002	0.0	0.000	-1	1	0.967	0.984	0.001	0.000	26.665	8.483	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.001	0.0	0.003	-1	1	1.000	1.000	0.000	0.000	14.704	8.398	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.034	0.002	0.0	0.000	1	1	0.967	0.984	0.001	0.001	22.795	8.305	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.0	0.001	1	1	1.000	1.000	0.000	0.000	4.229	2.494	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.037	0.001	0.0	0.000	1	5	0.977	0.981	0.001	0.000	41.227	13.681	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.0	0.001	1	5	1.000	1.000	0.000	0.000	4.464	2.777	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.071	0.002	0.0	0.000	1	100	0.979	0.983	0.008	0.001	8.616	0.603	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.0	0.001	1	100	1.000	1.000	0.000	0.000	4.586	1.924	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.041	0.002	0.0	0.000	-1	5	0.977	0.983	0.008	0.001	4.983	0.495	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.000	0.0	0.003	-1	5	1.000	1.000	0.000	0.000	15.535	6.701	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.062	0.003	0.0	0.000	-1	100	0.979	0.981	0.001	0.000	59.872	18.044	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.001	0.0	0.003	-1	100	1.000	1.000	0.000	0.000	18.539	11.564	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.764	0.0	0.628	0.0	k-means++	NaN	30	NaN	0.342	0.0	2.234	0.0	See	See
3	KMeans_tall	fit	1000000	1000000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.610	0.0	0.787	0.0	random	NaN	30	NaN	0.369	0.0	1.654	0.0	See	See
6	KMeans_tall	fit	1000000	1000000	100	42	20.0	200	sklearn.datasets.make_blobs	30	9.151	0.0	2.623	0.0	k-means++	NaN	30	NaN	4.243	0.0	2.157	0.0	See	See
9	KMeans_tall	fit	1000000	1000000	100	42	20.0	200	sklearn.datasets.make_blobs	30	8.037	0.0	2.986	0.0	random	NaN	30	NaN	4.492	0.0	1.789	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.255	0.000	k-means++	0.000	30	0.001	0.0	0.0	7.734	3.374	See	See
2	KMeans_tall	predict	1000000	1	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.000	0.002	k-means++	1.000	30	1.000	0.0	0.0	8.094	3.042	See	See
4	KMeans_tall	predict	1000000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.267	0.000	random	0.000	30	0.001	0.0	0.0	7.010	2.154	See	See
5	KMeans_tall	predict	1000000	1	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.000	0.002	random	1.000	30	1.000	0.0	0.0	9.557	3.981	See	See
7	KMeans_tall	predict	1000000	1000	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	11.796	0.000	k-means++	0.001	30	0.002	0.0	0.0	6.049	1.919	See	See
8	KMeans_tall	predict	1000000	1	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.013	0.002	k-means++	1.000	30	1.000	0.0	0.0	9.370	5.082	See	See
10	KMeans_tall	predict	1000000	1000	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	11.758	0.000	random	0.002	30	0.003	0.0	0.0	5.657	2.354	See	See
11	KMeans_tall	predict	1000000	1	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.013	0.002	random	1.000	30	1.000	0.0	0.0	9.449	5.307	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.325	0.0	0.010	0.0	k-means++	NaN	20	NaN	0.056	0.0	5.778	0.0	See	See
3	KMeans_short	fit	10000	10000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.106	0.0	0.030	0.0	random	NaN	20	NaN	0.191	0.0	0.554	0.0	See	See
6	KMeans_short	fit	10000	10000	100	42	13.0	200	sklearn.datasets.make_blobs	20	1.188	0.0	0.135	0.0	k-means++	NaN	20	NaN	0.308	0.0	3.860	0.0	See	See
9	KMeans_short	fit	10000	10000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.362	0.0	0.442	0.0	random	NaN	20	NaN	0.664	0.0	0.545	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.003	0.001	0.122	0.000	k-means++	0.004	20	-0.001	0.001	0.0	3.284	1.208	See	See
2	KMeans_short	predict	10000	1	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.000	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	8.531	4.667	See	See
4	KMeans_short	predict	10000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.000	0.138	0.000	random	0.002	20	0.000	0.001	0.0	2.738	0.617	See	See
5	KMeans_short	predict	10000	1	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.000	0.000	0.002	random	1.000	20	1.000	0.000	0.0	7.592	5.510	See	See
7	KMeans_short	predict	10000	1000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.004	0.000	4.494	0.000	k-means++	0.309	20	0.315	0.002	0.0	2.097	0.321	See	See
8	KMeans_short	predict	10000	1	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.002	0.000	0.008	0.002	k-means++	1.000	20	1.000	0.000	0.0	8.133	3.540	See	See
10	KMeans_short	predict	10000	1000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.004	0.000	4.515	0.000	random	0.286	20	0.359	0.002	0.0	2.121	0.303	See	See
11	KMeans_short	predict	10000	1	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.002	0.000	0.008	0.002	random	1.000	20	1.000	0.000	0.0	8.059	2.988	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_informative	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	5	5	0	42	sklearn.datasets.make_classification	[20]	17.673	0.0	[-0.06676297]	0.000	NaN	NaN	NaN	NaN	NaN	3.203	0.0	5.517	0.0	See	See
3	LogisticRegression	fit	1000	1000	10000	5	5	0	42	sklearn.datasets.make_classification	[26]	1.400	0.0	[1.48531349]	0.001	NaN	NaN	NaN	NaN	NaN	1.330	0.0	1.053	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_informative	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	5	5	0	42	sklearn.datasets.make_classification	[20]	0.000	0.0	[39.59390512]	0.0	NaN	NaN	NaN	NaN	0.519	0.000	0.000	0.837	0.450	See	See
2	LogisticRegression	predict	1000000	1	100	5	5	0	42	sklearn.datasets.make_classification	[20]	0.000	0.0	[0.13153492]	0.0	NaN	NaN	NaN	NaN	0.000	0.000	0.000	0.426	0.312	See	See
4	LogisticRegression	predict	1000	100	10000	5	5	0	42	sklearn.datasets.make_classification	[26]	0.003	0.0	[81.61674292]	0.0	NaN	NaN	NaN	NaN	0.310	0.004	0.001	0.570	0.094	See	See
5	LogisticRegression	predict	1000	1	10000	5	5	0	42	sklearn.datasets.make_classification	[26]	0.000	0.0	[13.63657223]	0.0	NaN	NaN	NaN	NaN	0.000	0.001	0.000	0.165	0.078	See	See

Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_informative	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	10	42	sklearn.datasets.make_regression	NaN	0.342	0.0	0.234	0.0	NaN	NaN	NaN	0.335	0.0	1.022	0.0	See	See
3	Ridge	fit	1000000	1000000	100	10	42	sklearn.datasets.make_regression	NaN	1.902	0.0	0.421	0.0	NaN	NaN	NaN	0.438	0.0	4.347	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_informative	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	10	42	sklearn.datasets.make_regression	NaN	0.013	0.001	6.301	0.0	NaN	NaN	0.104	0.022	0.001	0.568	0.036	See	See
2	Ridge	predict	1000	1	10000	10	42	sklearn.datasets.make_regression	NaN	0.000	0.000	0.566	0.0	NaN	NaN	NaN	0.000	0.000	0.775	0.469	See	See
4	Ridge	predict	1000000	1000	100	10	42	sklearn.datasets.make_regression	NaN	0.000	0.000	4.430	0.0	NaN	NaN	1.000	0.000	0.000	0.654	0.371	See	See
5	Ridge	predict	1000000	1	100	10	42	sklearn.datasets.make_regression	NaN	0.000	0.000	0.008	0.0	NaN	NaN	NaN	0.000	0.000	0.575	0.555	See	See